Analysis on the Impact of Fracturing Treatment Design and Reservoir Properties on Production from Shale Gas Reservoirs

Abstract

Production from shale gas reservoirs depends greatly on the efficiency of hydraulic fracturing treatments. The cumulated experience in the industry has led to several best practices in treatment design, which have improved productivity of these reservoirs. However, further advancement of treatment design requires a deeper understanding of the complex physics involved in both hydraulic fracturing and production, such as stress shadow, proppant placement and treatment interaction with pre-existing natural fractures. This paper sheds light on the non-linear physics involved in the production of shale gas reservoirs by improving the understanding of the complex relation between gas production, the reservoir properties, and several treatment design parameters. A fracturing-to-production simulation workflow integrating the Unconventional Fracture Model (Weng et al., 2011), with the Unconventional Production Model (Cohen et al., 2012) is presented. By applying this workflow to a realistic reservoir, we did an extensive parametric study to investigate the relation between production and treatment design parameters such as fracturing fluid viscosity, proppant size, proppant concentration, proppant injection order, treatment volume, pumping rate, pad size and hybrid treatment. The paper also evaluates the influence of unconventional reservoir properties -such as permeability, horizontal stress, horizontal stress anisotropy, horizontal stress orientation, Poisson's ratio and Young's modulus -on production. Since this paper focuses on fluid and proppant selection, our methodology was to run 28 simulations to cover the 2D parametric space of proppant size and fracturing fluid viscosity for all of these parameters. More than fourteen hundred simulations were run in this parametric study and the results provide guidelines for optimized treatment design. This paper illustrates how this unique workflow can identifies the optimum fluid and proppant selection that gives the maximum production for a given reservoir and completion. In addition, the parametric study shows how these optimums evolve as a function of reservoir and treatment parameters. The results validate several best practices in treatment design for shale. For example, combination of different sizes of proppant optimizes production by maximizing initial production and slowing down production decline. Simulations also confirm the best practice of injecting the smallest proppant first. The study explains why slickwater treatments should be injected at maximum pumping rate and preferably with 40/70 mesh sand. It also illustrates why reservoirs with high Young's modulus (such as the Barnett shale) can be stimulated effectively with slickwater. Another key finding is that the optimum fluid viscosity increases with treatment volume.

Effect of Flow Rate and Viscosity on Complex Fracture Development in UFM Model

“…The production part is done with the UPM model [28]. The base case for this Example 3 is from the paper [29] ( Table 5). shows the cumulated production after 3 years as a function of the proppant size and the fracturing fluid viscosity for the case when zero friction coefficient at NF is used and the two crossing models are applied.…”

Behavior of Titania Nanoparticles in Cross-linking Hydroxypropyl Guar Used in Hydraulic Fracturing Fluids For Oil Recovery

“…For this purpose, linear gels (water containing a gelling agent) exhibiting medium viscosity or even cross-linked gels (water viscosified by using a cross-linked gelling agent) of exceptionally high viscosity in the range of 100–1000 cP are used. They enable the transport of large size proppants (400–1200 μm) into those reservoirs. , …”

Applying complex fracture model and integrated workflow in unconventional reservoirs

“…Although single-porosity simulation with discrete fracture networks is numerically quite challenging, it provides more accurate results for ultra-tight formations than the dual-porosity approach. Cohen et al (2013) presented examples of using a semianalytical production model integrated with the UFM model to analyze and optimize fracturing parameters such as fluid viscosity and proppant size. However, simpler production models are limited in terms of their ability to accurately account for the effects of multiphase flow and cleanup of fracturing fluid, highly uneven vertical proppant distribution due to proppant settling, fracture conductivity change over the life of production, gas desorption, etc.…”